Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations3905
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 MiB
Average record size in memory712.1 B

Variable types

Numeric9
Text5
Categorical6
Boolean1

Alerts

Result has constant value "Pass" Constant
Backlog is highly overall correlated with TotalHigh correlation
CGPA is highly overall correlated with Overall Category and 2 other fieldsHigh correlation
Credit is highly overall correlated with External and 2 other fieldsHigh correlation
External is highly overall correlated with Credit and 5 other fieldsHigh correlation
Grade is highly overall correlated with External and 2 other fieldsHigh correlation
Internal is highly overall correlated with Credit and 2 other fieldsHigh correlation
Overall Category is highly overall correlated with CGPA and 2 other fieldsHigh correlation
Percentage is highly overall correlated with CGPA and 2 other fieldsHigh correlation
Point is highly overall correlated with External and 2 other fieldsHigh correlation
Semester is highly overall correlated with External and 1 other fieldsHigh correlation
Total is highly overall correlated with Backlog and 6 other fieldsHigh correlation
Total Marks is highly overall correlated with CGPA and 2 other fieldsHigh correlation
Backlog is highly imbalanced (81.4%) Imbalance
Total has 111 (2.8%) zeros Zeros

Reproduction

Analysis started2025-06-08 03:13:31.686600
Analysis finished2025-06-08 03:14:01.052826
Duration29.37 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Seat No
Real number (ℝ)

Distinct63
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2203445.6
Minimum2201036
Maximum2207074
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2025-06-08T08:44:01.465131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2201036
5-th percentile2203058
Q12203085
median2203482
Q32203499
95-th percentile2203511
Maximum2207074
Range6038
Interquartile range (IQR)414

Descriptive statistics

Standard deviation783.8584
Coefficient of variation (CV)0.00035574212
Kurtosis13.536672
Mean2203445.6
Median Absolute Deviation (MAD)27
Skewness2.8686726
Sum8.604455 × 109
Variance614434
MonotonicityIncreasing
2025-06-08T08:44:02.109872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2201036 62
 
1.6%
2203499 62
 
1.6%
2203485 62
 
1.6%
2203486 62
 
1.6%
2203487 62
 
1.6%
2203488 62
 
1.6%
2203490 62
 
1.6%
2203491 62
 
1.6%
2203492 62
 
1.6%
2203493 62
 
1.6%
Other values (53) 3285
84.1%
ValueCountFrequency (%)
2201036 62
1.6%
2203050 62
1.6%
2203054 62
1.6%
2203058 62
1.6%
2203073 62
1.6%
2203074 61
1.6%
2203075 62
1.6%
2203076 62
1.6%
2203077 62
1.6%
2203078 62
1.6%
ValueCountFrequency (%)
2207074 62
1.6%
2207071 62
1.6%
2205321 62
1.6%
2203511 62
1.6%
2203510 62
1.6%
2203509 62
1.6%
2203508 62
1.6%
2203507 62
1.6%
2203506 62
1.6%
2203505 62
1.6%

Name
Text

Distinct63
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size299.1 KiB
2025-06-08T08:44:03.454026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length28
Median length24
Mean length21.443278
Min length14

Characters and Unicode

Total characters83736
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLANDGE SAKSHI CHANGDEV
2nd rowLANDGE SAKSHI CHANGDEV
3rd rowLANDGE SAKSHI CHANGDEV
4th rowLANDGE SAKSHI CHANGDEV
5th rowLANDGE SAKSHI CHANGDEV
ValueCountFrequency (%)
sanjay 310
 
2.6%
navnath 248
 
2.1%
santosh 248
 
2.1%
vaishnavi 186
 
1.6%
kiran 186
 
1.6%
rajendra 186
 
1.6%
rahul 186
 
1.6%
sanika 124
 
1.1%
vikram 124
 
1.1%
more 124
 
1.1%
Other values (144) 9793
83.6%
2025-06-08T08:44:05.244861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 16550
19.8%
7810
 
9.3%
H 6508
 
7.8%
R 5578
 
6.7%
S 5392
 
6.4%
N 5143
 
6.1%
E 5020
 
6.0%
I 4030
 
4.8%
K 3719
 
4.4%
D 3470
 
4.1%
Other values (14) 20516
24.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 75926
90.7%
Space Separator 7810
 
9.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 16550
21.8%
H 6508
 
8.6%
R 5578
 
7.3%
S 5392
 
7.1%
N 5143
 
6.8%
E 5020
 
6.6%
I 4030
 
5.3%
K 3719
 
4.9%
D 3470
 
4.6%
T 2604
 
3.4%
Other values (13) 17912
23.6%
Space Separator
ValueCountFrequency (%)
7810
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 75926
90.7%
Common 7810
 
9.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 16550
21.8%
H 6508
 
8.6%
R 5578
 
7.3%
S 5392
 
7.1%
N 5143
 
6.8%
E 5020
 
6.6%
I 4030
 
5.3%
K 3719
 
4.9%
D 3470
 
4.6%
T 2604
 
3.4%
Other values (13) 17912
23.6%
Common
ValueCountFrequency (%)
7810
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 83736
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 16550
19.8%
7810
 
9.3%
H 6508
 
7.8%
R 5578
 
6.7%
S 5392
 
6.4%
N 5143
 
6.1%
E 5020
 
6.0%
I 4030
 
4.8%
K 3719
 
4.4%
D 3470
 
4.1%
Other values (14) 20516
24.5%
Distinct53
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size255.9 KiB
2025-06-08T08:44:06.210970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length77
Median length23
Mean length10.095775
Min length4

Characters and Unicode

Total characters39424
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowARCHANA
2nd rowARCHANA
3rd rowARCHANA
4th rowARCHANA
5th rowARCHANA
ValueCountFrequency (%)
code 372
 
5.2%
name 372
 
5.2%
cie 372
 
5.2%
ese 372
 
5.2%
tot 372
 
5.2%
gr 372
 
5.2%
cr 372
 
5.2%
pt 372
 
5.2%
subject 372
 
5.2%
asha 124
 
1.7%
Other values (53) 3719
51.7%
2025-06-08T08:44:08.217265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 7128
18.1%
3286
 
8.3%
E 3162
 
8.0%
I 2788
 
7.1%
T 2666
 
6.8%
S 2665
 
6.8%
N 2418
 
6.1%
R 1922
 
4.9%
H 1673
 
4.2%
C 1550
 
3.9%
Other values (13) 10166
25.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 36138
91.7%
Space Separator 3286
 
8.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 7128
19.7%
E 3162
 
8.7%
I 2788
 
7.7%
T 2666
 
7.4%
S 2665
 
7.4%
N 2418
 
6.7%
R 1922
 
5.3%
H 1673
 
4.6%
C 1550
 
4.3%
O 1054
 
2.9%
Other values (12) 9112
25.2%
Space Separator
ValueCountFrequency (%)
3286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 36138
91.7%
Common 3286
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 7128
19.7%
E 3162
 
8.7%
I 2788
 
7.7%
T 2666
 
7.4%
S 2665
 
7.4%
N 2418
 
6.7%
R 1922
 
5.3%
H 1673
 
4.6%
C 1550
 
4.3%
O 1054
 
2.9%
Other values (12) 9112
25.2%
Common
ValueCountFrequency (%)
3286
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 7128
18.1%
3286
 
8.3%
E 3162
 
8.0%
I 2788
 
7.1%
T 2666
 
6.8%
S 2665
 
6.8%
N 2418
 
6.1%
R 1922
 
4.9%
H 1673
 
4.2%
C 1550
 
3.9%
Other values (13) 10166
25.8%

PRN No
Text

Distinct63
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size255.5 KiB
2025-06-08T08:44:09.268298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters39050
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSU00001852
2nd rowSU00001852
3rd rowSU00001852
4th rowSU00001852
5th rowSU00001852
ValueCountFrequency (%)
su00001852 62
 
1.6%
su00001827 62
 
1.6%
su00001854 62
 
1.6%
su00001828 62
 
1.6%
su00001843 62
 
1.6%
su00001837 62
 
1.6%
su00001876 62
 
1.6%
su00001867 62
 
1.6%
su00001839 62
 
1.6%
su00001841 62
 
1.6%
Other values (53) 3285
84.1%
2025-06-08T08:44:10.584677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 16054
41.1%
8 4525
 
11.6%
1 4463
 
11.4%
S 3905
 
10.0%
U 3905
 
10.0%
2 1054
 
2.7%
3 1053
 
2.7%
6 991
 
2.5%
4 930
 
2.4%
7 930
 
2.4%
Other values (2) 1240
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31240
80.0%
Uppercase Letter 7810
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16054
51.4%
8 4525
 
14.5%
1 4463
 
14.3%
2 1054
 
3.4%
3 1053
 
3.4%
6 991
 
3.2%
4 930
 
3.0%
7 930
 
3.0%
5 868
 
2.8%
9 372
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
S 3905
50.0%
U 3905
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 31240
80.0%
Latin 7810
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16054
51.4%
8 4525
 
14.5%
1 4463
 
14.3%
2 1054
 
3.4%
3 1053
 
3.4%
6 991
 
3.2%
4 930
 
3.0%
7 930
 
3.0%
5 868
 
2.8%
9 372
 
1.2%
Latin
ValueCountFrequency (%)
S 3905
50.0%
U 3905
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39050
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16054
41.1%
8 4525
 
11.6%
1 4463
 
11.4%
S 3905
 
10.0%
U 3905
 
10.0%
2 1054
 
2.7%
3 1053
 
2.7%
6 991
 
2.5%
4 930
 
2.4%
7 930
 
2.4%
Other values (2) 1240
 
3.2%
Distinct62
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size303.3 KiB
2025-06-08T08:44:11.797004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length41
Median length34
Mean length22.543662
Min length5

Characters and Unicode

Total characters88033
Distinct characters42
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBASIC PROGRAMMING IN C
2nd rowDATABASE MANAGEMENT SYSTEM
3rd rowELEMENTS OF INFORMATION TECHNOLOGY
4th rowINTRODUCTION TO R PROGRAMMING
5th rowINTRODUCTION TO DATA
ValueCountFrequency (%)
lab 1323
 
9.9%
on 1323
 
9.9%
course 1260
 
9.4%
data 565
 
4.2%
and 314
 
2.4%
programming 252
 
1.9%
artificial 252
 
1.9%
intelligence 189
 
1.4%
introduction 189
 
1.4%
to 189
 
1.4%
Other values (103) 7494
56.1%
2025-06-08T08:44:13.924785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9194
 
10.4%
A 7616
 
8.7%
N 6801
 
7.7%
I 6422
 
7.3%
E 6362
 
7.2%
O 5920
 
6.7%
T 5413
 
6.1%
S 4849
 
5.5%
C 4725
 
5.4%
R 4598
 
5.2%
Other values (32) 26133
29.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 72351
82.2%
Space Separator 9194
 
10.4%
Decimal Number 5607
 
6.4%
Other Punctuation 567
 
0.6%
Control 251
 
0.3%
Dash Punctuation 63
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 7616
10.5%
N 6801
9.4%
I 6422
8.9%
E 6362
8.8%
O 5920
 
8.2%
T 5413
 
7.5%
S 4849
 
6.7%
C 4725
 
6.5%
R 4598
 
6.4%
L 4218
 
5.8%
Other values (16) 15427
21.3%
Decimal Number
ValueCountFrequency (%)
0 1764
31.5%
1 1008
18.0%
2 756
13.5%
3 504
 
9.0%
4 504
 
9.0%
6 378
 
6.7%
5 315
 
5.6%
8 126
 
2.2%
7 126
 
2.2%
9 126
 
2.2%
Other Punctuation
ValueCountFrequency (%)
, 441
77.8%
. 63
 
11.1%
: 63
 
11.1%
Space Separator
ValueCountFrequency (%)
9194
100.0%
Control
ValueCountFrequency (%)
251
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 63
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 72351
82.2%
Common 15682
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 7616
10.5%
N 6801
9.4%
I 6422
8.9%
E 6362
8.8%
O 5920
 
8.2%
T 5413
 
7.5%
S 4849
 
6.7%
C 4725
 
6.5%
R 4598
 
6.4%
L 4218
 
5.8%
Other values (16) 15427
21.3%
Common
ValueCountFrequency (%)
9194
58.6%
0 1764
 
11.2%
1 1008
 
6.4%
2 756
 
4.8%
3 504
 
3.2%
4 504
 
3.2%
, 441
 
2.8%
6 378
 
2.4%
5 315
 
2.0%
251
 
1.6%
Other values (6) 567
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9194
 
10.4%
A 7616
 
8.7%
N 6801
 
7.7%
I 6422
 
7.3%
E 6362
 
7.2%
O 5920
 
6.7%
T 5413
 
6.1%
S 4849
 
5.5%
C 4725
 
5.4%
R 4598
 
5.2%
Other values (32) 26133
29.7%

CODE
Text

Distinct62
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size256.5 KiB
2025-06-08T08:44:14.836362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length10.257875
Min length10

Characters and Unicode

Total characters40057
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBSC-DS101T
2nd rowBSC-DS102T
3rd rowBSC-DS103T
4th rowBSC-DS104T
5th rowBSC-DS105T
ValueCountFrequency (%)
bsc-ds 1007
 
20.5%
bsc-ds101t 63
 
1.3%
bsc-ds205t 63
 
1.3%
bsc-ds103t 63
 
1.3%
bsc-ds104t 63
 
1.3%
bsc-ds105t 63
 
1.3%
bsc-ds106t 63
 
1.3%
bsc-ds107t 63
 
1.3%
bsc-ds108t 63
 
1.3%
bsc-ds109p 63
 
1.3%
Other values (53) 3338
68.0%
2025-06-08T08:44:17.133381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 7810
19.5%
B 3905
9.7%
C 3905
9.7%
- 3905
9.7%
D 3905
9.7%
0 3527
8.8%
T 2519
 
6.3%
1 2079
 
5.2%
P 1386
 
3.5%
3 1134
 
2.8%
Other values (8) 5982
14.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 23430
58.5%
Decimal Number 11715
29.2%
Dash Punctuation 3905
 
9.7%
Space Separator 1007
 
2.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3527
30.1%
1 2079
17.7%
3 1134
 
9.7%
2 1133
 
9.7%
4 1071
 
9.1%
5 881
 
7.5%
6 819
 
7.0%
7 378
 
3.2%
9 378
 
3.2%
8 315
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
S 7810
33.3%
B 3905
16.7%
C 3905
16.7%
D 3905
16.7%
T 2519
 
10.8%
P 1386
 
5.9%
Dash Punctuation
ValueCountFrequency (%)
- 3905
100.0%
Space Separator
ValueCountFrequency (%)
1007
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23430
58.5%
Common 16627
41.5%

Most frequent character per script

Common
ValueCountFrequency (%)
- 3905
23.5%
0 3527
21.2%
1 2079
12.5%
3 1134
 
6.8%
2 1133
 
6.8%
4 1071
 
6.4%
1007
 
6.1%
5 881
 
5.3%
6 819
 
4.9%
7 378
 
2.3%
Other values (2) 693
 
4.2%
Latin
ValueCountFrequency (%)
S 7810
33.3%
B 3905
16.7%
C 3905
16.7%
D 3905
16.7%
T 2519
 
10.8%
P 1386
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40057
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 7810
19.5%
B 3905
9.7%
C 3905
9.7%
- 3905
9.7%
D 3905
9.7%
0 3527
8.8%
T 2519
 
6.3%
1 2079
 
5.2%
P 1386
 
3.5%
3 1134
 
2.8%
Other values (8) 5982
14.9%

Internal
Real number (ℝ)

High correlation 

Distinct25
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.286044
Minimum6
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.8 KiB
2025-06-08T08:44:17.567403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile9
Q110
median12
Q314
95-th percentile25
Maximum30
Range24
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.8387283
Coefficient of variation (CV)0.36419633
Kurtosis2.459239
Mean13.286044
Median Absolute Deviation (MAD)2
Skewness1.772461
Sum51882
Variance23.413291
MonotonicityNot monotonic
2025-06-08T08:44:17.951781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
10 602
15.4%
11 596
15.3%
12 572
14.6%
13 498
12.8%
9 423
10.8%
14 416
10.7%
15 128
 
3.3%
20 75
 
1.9%
23 67
 
1.7%
28 58
 
1.5%
Other values (15) 470
12.0%
ValueCountFrequency (%)
6 2
 
0.1%
7 41
 
1.0%
8 45
 
1.2%
9 423
10.8%
10 602
15.4%
11 596
15.3%
12 572
14.6%
13 498
12.8%
14 416
10.7%
15 128
 
3.3%
ValueCountFrequency (%)
30 20
 
0.5%
29 26
 
0.7%
28 58
1.5%
27 47
1.2%
26 33
0.8%
25 52
1.3%
24 58
1.5%
23 67
1.7%
22 49
1.3%
21 40
1.0%

External
Real number (ℝ)

High correlation 

Distinct56
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.46274
Minimum14
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.8 KiB
2025-06-08T08:44:18.412375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile14
Q120
median27
Q332
95-th percentile49.8
Maximum69
Range55
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.17567
Coefficient of variation (CV)0.37052641
Kurtosis2.3277528
Mean27.46274
Median Absolute Deviation (MAD)6
Skewness1.313932
Sum107242
Variance103.54427
MonotonicityNot monotonic
2025-06-08T08:44:18.839726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 248
 
6.4%
28 229
 
5.9%
30 225
 
5.8%
29 196
 
5.0%
20 192
 
4.9%
25 190
 
4.9%
34 172
 
4.4%
33 163
 
4.2%
32 158
 
4.0%
31 157
 
4.0%
Other values (46) 1975
50.6%
ValueCountFrequency (%)
14 248
6.4%
15 110
2.8%
16 109
2.8%
17 140
3.6%
18 128
3.3%
19 111
2.8%
20 192
4.9%
21 153
3.9%
22 140
3.6%
23 151
3.9%
ValueCountFrequency (%)
69 3
 
0.1%
68 2
 
0.1%
67 3
 
0.1%
66 10
0.3%
65 9
0.2%
64 9
0.2%
63 3
 
0.1%
62 8
0.2%
61 8
0.2%
60 15
0.4%

Total
Real number (ℝ)

High correlation  Zeros 

Distinct79
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.9137
Minimum0
Maximum98
Zeros111
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size45.8 KiB
2025-06-08T08:44:19.284827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24
Q131
median38
Q345
95-th percentile74
Maximum98
Range98
Interquartile range (IQR)14

Descriptive statistics

Standard deviation15.570865
Coefficient of variation (CV)0.39011328
Kurtosis2.1564852
Mean39.9137
Median Absolute Deviation (MAD)7
Skewness0.81065769
Sum155863
Variance242.45182
MonotonicityNot monotonic
2025-06-08T08:44:19.718960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 163
 
4.2%
41 158
 
4.0%
39 149
 
3.8%
34 141
 
3.6%
37 140
 
3.6%
32 140
 
3.6%
40 140
 
3.6%
35 138
 
3.5%
43 137
 
3.5%
33 129
 
3.3%
Other values (69) 2470
63.3%
ValueCountFrequency (%)
0 111
2.8%
21 10
 
0.3%
22 11
 
0.3%
23 57
1.5%
24 81
2.1%
25 91
2.3%
26 106
2.7%
27 126
3.2%
28 110
2.8%
29 119
3.0%
ValueCountFrequency (%)
98 1
 
< 0.1%
97 3
 
0.1%
96 1
 
< 0.1%
95 2
 
0.1%
94 3
 
0.1%
93 3
 
0.1%
92 4
0.1%
91 8
0.2%
90 7
0.2%
89 8
0.2%

Grade
Categorical

High correlation 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size222.9 KiB
A+
1286 
A
1168 
B+
480 
O
461 
B
296 
Other values (2)
214 

Length

Max length2
Median length1
Mean length1.4522407
Min length1

Characters and Unicode

Total characters5671
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA+
2nd rowA
3rd rowA
4th rowA
5th rowA+

Common Values

ValueCountFrequency (%)
A+ 1286
32.9%
A 1168
29.9%
B+ 480
 
12.3%
O 461
 
11.8%
B 296
 
7.6%
C 195
 
5.0%
D 19
 
0.5%

Length

2025-06-08T08:44:20.099601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-08T08:44:20.455138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
a 2454
62.8%
b 776
 
19.9%
o 461
 
11.8%
c 195
 
5.0%
d 19
 
0.5%

Most occurring characters

ValueCountFrequency (%)
A 2454
43.3%
+ 1766
31.1%
B 776
 
13.7%
O 461
 
8.1%
C 195
 
3.4%
D 19
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3905
68.9%
Math Symbol 1766
31.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2454
62.8%
B 776
 
19.9%
O 461
 
11.8%
C 195
 
5.0%
D 19
 
0.5%
Math Symbol
ValueCountFrequency (%)
+ 1766
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3905
68.9%
Common 1766
31.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2454
62.8%
B 776
 
19.9%
O 461
 
11.8%
C 195
 
5.0%
D 19
 
0.5%
Common
ValueCountFrequency (%)
+ 1766
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5671
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 2454
43.3%
+ 1766
31.1%
B 776
 
13.7%
O 461
 
8.1%
C 195
 
3.4%
D 19
 
0.3%

Credit
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size221.2 KiB
2
3276 
4
377 
3
 
252

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3905
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 3276
83.9%
4 377
 
9.7%
3 252
 
6.5%

Length

2025-06-08T08:44:20.849883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-08T08:44:21.132873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 3276
83.9%
4 377
 
9.7%
3 252
 
6.5%

Most occurring characters

ValueCountFrequency (%)
2 3276
83.9%
4 377
 
9.7%
3 252
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3905
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3276
83.9%
4 377
 
9.7%
3 252
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Common 3905
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 3276
83.9%
4 377
 
9.7%
3 252
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3276
83.9%
4 377
 
9.7%
3 252
 
6.5%

Point
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1216389
Minimum4
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.8 KiB
2025-06-08T08:44:21.414834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q17
median8
Q39
95-th percentile10
Maximum10
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3193102
Coefficient of variation (CV)0.16244383
Kurtosis0.15294126
Mean8.1216389
Median Absolute Deviation (MAD)1
Skewness-0.76354943
Sum31715
Variance1.7405793
MonotonicityNot monotonic
2025-06-08T08:44:21.717932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
9 1286
32.9%
8 1168
29.9%
7 480
 
12.3%
10 461
 
11.8%
6 296
 
7.6%
5 195
 
5.0%
4 19
 
0.5%
ValueCountFrequency (%)
4 19
 
0.5%
5 195
 
5.0%
6 296
 
7.6%
7 480
 
12.3%
8 1168
29.9%
9 1286
32.9%
10 461
 
11.8%
ValueCountFrequency (%)
10 461
 
11.8%
9 1286
32.9%
8 1168
29.9%
7 480
 
12.3%
6 296
 
7.6%
5 195
 
5.0%
4 19
 
0.5%

Total Marks
Real number (ℝ)

High correlation 

Distinct62
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2527.0412
Minimum2137
Maximum3127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2025-06-08T08:44:22.098838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2137
5-th percentile2168
Q12281
median2506
Q32713
95-th percentile2971
Maximum3127
Range990
Interquartile range (IQR)432

Descriptive statistics

Standard deviation260.18206
Coefficient of variation (CV)0.10295917
Kurtosis-0.87697874
Mean2527.0412
Median Absolute Deviation (MAD)223
Skewness0.34598051
Sum9868096
Variance67694.707
MonotonicityNot monotonic
2025-06-08T08:44:22.517646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2271 124
 
3.2%
2831 62
 
1.6%
2533 62
 
1.6%
2148 62
 
1.6%
2506 62
 
1.6%
2597 62
 
1.6%
2636 62
 
1.6%
2877 62
 
1.6%
2686 62
 
1.6%
3127 62
 
1.6%
Other values (52) 3223
82.5%
ValueCountFrequency (%)
2137 62
1.6%
2148 62
1.6%
2154 62
1.6%
2168 62
1.6%
2179 62
1.6%
2188 62
1.6%
2198 62
1.6%
2210 62
1.6%
2232 62
1.6%
2246 62
1.6%
ValueCountFrequency (%)
3127 62
1.6%
3048 61
1.6%
3042 62
1.6%
2971 62
1.6%
2929 62
1.6%
2877 62
1.6%
2876 62
1.6%
2863 62
1.6%
2857 62
1.6%
2838 62
1.6%

Percentage
Real number (ℝ)

High correlation 

Distinct62
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.195818
Minimum59.36
Maximum86.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2025-06-08T08:44:22.999452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum59.36
5-th percentile60.22
Q163.36
median69.61
Q375.36
95-th percentile82.53
Maximum86.86
Range27.5
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.2269692
Coefficient of variation (CV)0.10295441
Kurtosis-0.87660712
Mean70.195818
Median Absolute Deviation (MAD)6.19
Skewness0.34602984
Sum274114.67
Variance52.229084
MonotonicityNot monotonic
2025-06-08T08:44:23.701231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63.08 124
 
3.2%
78.64 62
 
1.6%
70.36 62
 
1.6%
59.67 62
 
1.6%
69.61 62
 
1.6%
72.14 62
 
1.6%
73.22 62
 
1.6%
79.92 62
 
1.6%
74.61 62
 
1.6%
86.86 62
 
1.6%
Other values (52) 3223
82.5%
ValueCountFrequency (%)
59.36 62
1.6%
59.67 62
1.6%
59.83 62
1.6%
60.22 62
1.6%
60.53 62
1.6%
60.78 62
1.6%
61.06 62
1.6%
61.39 62
1.6%
62 62
1.6%
62.39 62
1.6%
ValueCountFrequency (%)
86.86 62
1.6%
84.67 61
1.6%
84.5 62
1.6%
82.53 62
1.6%
81.36 62
1.6%
79.92 62
1.6%
79.89 62
1.6%
79.53 62
1.6%
79.36 62
1.6%
78.83 62
1.6%

CGPA
Real number (ℝ)

High correlation 

Distinct55
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1220845
Minimum7.05
Maximum9.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2025-06-08T08:44:24.117238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7.05
5-th percentile7.14
Q17.53
median8.14
Q38.6
95-th percentile9.16
Maximum9.41
Range2.36
Interquartile range (IQR)1.07

Descriptive statistics

Standard deviation0.63561188
Coefficient of variation (CV)0.078257236
Kurtosis-1.0544963
Mean8.1220845
Median Absolute Deviation (MAD)0.54
Skewness0.06723037
Sum31716.74
Variance0.40400247
MonotonicityNot monotonic
2025-06-08T08:44:24.565404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.9 124
 
3.2%
7.53 124
 
3.2%
7.89 124
 
3.2%
8.51 124
 
3.2%
7.52 124
 
3.2%
8.58 124
 
3.2%
8.14 124
 
3.2%
7.65 124
 
3.2%
8.75 62
 
1.6%
8.98 62
 
1.6%
Other values (45) 2789
71.4%
ValueCountFrequency (%)
7.05 62
1.6%
7.06 62
1.6%
7.11 62
1.6%
7.14 62
1.6%
7.22 62
1.6%
7.24 62
1.6%
7.27 62
1.6%
7.28 62
1.6%
7.3 62
1.6%
7.41 62
1.6%
ValueCountFrequency (%)
9.41 62
1.6%
9.28 61
1.6%
9.24 62
1.6%
9.16 62
1.6%
9.09 62
1.6%
8.98 62
1.6%
8.95 62
1.6%
8.9 124
3.2%
8.84 62
1.6%
8.77 62
1.6%

Result
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size232.6 KiB
Pass
3905 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters15620
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPass
2nd rowPass
3rd rowPass
4th rowPass
5th rowPass

Common Values

ValueCountFrequency (%)
Pass 3905
100.0%

Length

2025-06-08T08:44:24.968419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-08T08:44:25.234396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
pass 3905
100.0%

Most occurring characters

ValueCountFrequency (%)
s 7810
50.0%
P 3905
25.0%
a 3905
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11715
75.0%
Uppercase Letter 3905
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 7810
66.7%
a 3905
33.3%
Uppercase Letter
ValueCountFrequency (%)
P 3905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15620
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 7810
50.0%
P 3905
25.0%
a 3905
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 7810
50.0%
P 3905
25.0%
a 3905
25.0%

Gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
Female
2232 
Male
1673 

Length

Max length6
Median length6
Mean length5.1431498
Min length4

Characters and Unicode

Total characters20084
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 2232
57.2%
Male 1673
42.8%

Length

2025-06-08T08:44:25.617744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-08T08:44:25.950118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
female 2232
57.2%
male 1673
42.8%

Most occurring characters

ValueCountFrequency (%)
e 6137
30.6%
a 3905
19.4%
l 3905
19.4%
F 2232
 
11.1%
m 2232
 
11.1%
M 1673
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16179
80.6%
Uppercase Letter 3905
 
19.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6137
37.9%
a 3905
24.1%
l 3905
24.1%
m 2232
 
13.8%
Uppercase Letter
ValueCountFrequency (%)
F 2232
57.2%
M 1673
42.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 20084
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6137
30.6%
a 3905
19.4%
l 3905
19.4%
F 2232
 
11.1%
m 2232
 
11.1%
M 1673
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20084
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6137
30.6%
a 3905
19.4%
l 3905
19.4%
F 2232
 
11.1%
m 2232
 
11.1%
M 1673
 
8.3%

Semester
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3060179
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.0 KiB
2025-06-08T08:44:26.364638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.632416
Coefficient of variation (CV)0.49377106
Kurtosis-1.1062258
Mean3.3060179
Median Absolute Deviation (MAD)1
Skewness0.14659598
Sum12910
Variance2.6647819
MonotonicityNot monotonic
2025-06-08T08:44:26.697571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 756
19.4%
3 756
19.4%
1 693
17.7%
2 693
17.7%
6 504
12.9%
5 503
12.9%
ValueCountFrequency (%)
1 693
17.7%
2 693
17.7%
3 756
19.4%
4 756
19.4%
5 503
12.9%
6 504
12.9%
ValueCountFrequency (%)
6 504
12.9%
5 503
12.9%
4 756
19.4%
3 756
19.4%
2 693
17.7%
1 693
17.7%

Backlog
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.3 KiB
False
3794 
True
 
111
ValueCountFrequency (%)
False 3794
97.2%
True 111
 
2.8%
2025-06-08T08:44:26.983897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Type
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size244.3 KiB
Theory
2519 
Practical
1386 

Length

Max length9
Median length6
Mean length7.0647887
Min length6

Characters and Unicode

Total characters27588
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTheory
2nd rowTheory
3rd rowTheory
4th rowTheory
5th rowTheory

Common Values

ValueCountFrequency (%)
Theory 2519
64.5%
Practical 1386
35.5%

Length

2025-06-08T08:44:27.314724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-08T08:44:27.631577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
theory 2519
64.5%
practical 1386
35.5%

Most occurring characters

ValueCountFrequency (%)
r 3905
14.2%
a 2772
10.0%
c 2772
10.0%
T 2519
9.1%
h 2519
9.1%
e 2519
9.1%
o 2519
9.1%
y 2519
9.1%
P 1386
 
5.0%
t 1386
 
5.0%
Other values (2) 2772
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23683
85.8%
Uppercase Letter 3905
 
14.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 3905
16.5%
a 2772
11.7%
c 2772
11.7%
h 2519
10.6%
e 2519
10.6%
o 2519
10.6%
y 2519
10.6%
t 1386
 
5.9%
i 1386
 
5.9%
l 1386
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
T 2519
64.5%
P 1386
35.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 27588
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 3905
14.2%
a 2772
10.0%
c 2772
10.0%
T 2519
9.1%
h 2519
9.1%
e 2519
9.1%
o 2519
9.1%
y 2519
9.1%
P 1386
 
5.0%
t 1386
 
5.0%
Other values (2) 2772
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27588
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 3905
14.2%
a 2772
10.0%
c 2772
10.0%
T 2519
9.1%
h 2519
9.1%
e 2519
9.1%
o 2519
9.1%
y 2519
9.1%
P 1386
 
5.0%
t 1386
 
5.0%
Other values (2) 2772
10.0%

Overall Category
Categorical

High correlation 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size259.5 KiB
First Class
2480 
Distinction
1239 
Second Class
 
186

Length

Max length12
Median length11
Mean length11.047631
Min length11

Characters and Unicode

Total characters43141
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDistinction
2nd rowDistinction
3rd rowDistinction
4th rowDistinction
5th rowDistinction

Common Values

ValueCountFrequency (%)
First Class 2480
63.5%
Distinction 1239
31.7%
Second Class 186
 
4.8%

Length

2025-06-08T08:44:27.966233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-08T08:44:28.298454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
class 2666
40.6%
first 2480
37.7%
distinction 1239
18.9%
second 186
 
2.8%

Most occurring characters

ValueCountFrequency (%)
s 9051
21.0%
i 6197
14.4%
t 4958
11.5%
2666
 
6.2%
C 2666
 
6.2%
l 2666
 
6.2%
a 2666
 
6.2%
n 2664
 
6.2%
F 2480
 
5.7%
r 2480
 
5.7%
Other values (6) 4647
10.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 33904
78.6%
Uppercase Letter 6571
 
15.2%
Space Separator 2666
 
6.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 9051
26.7%
i 6197
18.3%
t 4958
14.6%
l 2666
 
7.9%
a 2666
 
7.9%
n 2664
 
7.9%
r 2480
 
7.3%
c 1425
 
4.2%
o 1425
 
4.2%
e 186
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
C 2666
40.6%
F 2480
37.7%
D 1239
18.9%
S 186
 
2.8%
Space Separator
ValueCountFrequency (%)
2666
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 40475
93.8%
Common 2666
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 9051
22.4%
i 6197
15.3%
t 4958
12.2%
C 2666
 
6.6%
l 2666
 
6.6%
a 2666
 
6.6%
n 2664
 
6.6%
F 2480
 
6.1%
r 2480
 
6.1%
c 1425
 
3.5%
Other values (5) 3222
 
8.0%
Common
ValueCountFrequency (%)
2666
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43141
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 9051
21.0%
i 6197
14.4%
t 4958
11.5%
2666
 
6.2%
C 2666
 
6.2%
l 2666
 
6.2%
a 2666
 
6.2%
n 2664
 
6.2%
F 2480
 
5.7%
r 2480
 
5.7%
Other values (6) 4647
10.8%

Interactions

2025-06-08T08:43:56.797241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:34.291869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:37.068592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:39.602231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:42.498490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:45.010606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:48.667373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:51.661262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:54.252507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:57.116801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:34.647207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:37.370421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:39.912916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:42.801530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:46.340042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:48.979230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:51.963571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:54.617502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:57.412925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:34.960785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:37.651856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:40.281129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:43.074854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:46.618721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:49.255771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:52.245862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:54.957202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:57.716722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:35.253474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:37.941156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:40.747272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:43.346679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:46.979950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:49.555121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:52.522141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:55.269440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:58.059418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:35.545322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:38.217734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:41.073445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:43.613155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:47.257343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:49.831615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:52.795830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:55.487902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:58.407180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:35.857175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:38.503831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:41.367471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:43.897703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:47.539028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:50.176840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:53.082752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:55.766190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:58.736702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:36.151510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:38.774174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:41.645736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:44.171016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:47.812469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:50.583972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:53.352849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:55.973577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:59.048380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:36.453275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:39.051601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:41.942880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:44.439770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:48.097279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:51.070511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:53.630466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:56.206233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:59.281550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:36.771192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:39.330092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:42.225058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:44.725307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:48.388999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:51.382765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:53.911261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-08T08:43:56.499040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-06-08T08:44:28.582420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
BacklogCGPACreditExternalGenderGradeInternalOverall CategoryPercentagePointSeat NoSemesterTotalTotal MarksType
Backlog1.0000.1940.0570.1880.0000.1790.1300.1170.1940.1790.0430.1650.9990.1940.092
CGPA0.1941.0000.0000.3780.4170.2210.3050.7770.9960.4980.005-0.0000.3900.9960.000
Credit0.0570.0001.0000.6630.0000.2140.7470.0000.0000.2140.0000.4490.7630.0000.324
External0.1880.3780.6631.0000.0690.5110.6230.2420.3780.6750.0080.5190.9630.3780.306
Gender0.0000.4170.0000.0691.0000.0610.0870.2670.4670.0610.2380.0000.0230.4670.000
Grade0.1790.2210.2140.5110.0611.0000.2810.3090.2201.0000.0530.1810.5120.2200.260
Internal0.1300.3050.7470.6230.0870.2811.0000.2220.3060.350-0.0160.3710.7550.3060.326
Overall Category0.1170.7770.0000.2420.2670.3090.2221.0000.7660.3090.2160.0000.2770.7660.000
Percentage0.1940.9960.0000.3780.4670.2200.3060.7661.0000.4970.002-0.0000.3911.0000.000
Point0.1790.4980.2140.6750.0611.0000.3500.3090.4971.0000.0090.3200.6310.4970.260
Seat No0.0430.0050.0000.0080.2380.053-0.0160.2160.0020.0091.0000.0000.0050.0020.000
Semester0.165-0.0000.4490.5190.0000.1810.3710.000-0.0000.3200.0001.0000.527-0.0000.184
Total0.9990.3900.7630.9630.0230.5120.7550.2770.3910.6310.0050.5271.0000.3910.370
Total Marks0.1940.9960.0000.3780.4670.2200.3060.7661.0000.4970.002-0.0000.3911.0000.000
Type0.0920.0000.3240.3060.0000.2600.3260.0000.0000.2600.0000.1840.3700.0001.000

Missing values

2025-06-08T08:43:59.670817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-08T08:44:00.619529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Seat NoNameMother's NamePRN NoSUBJECT NAMECODEInternalExternalTotalGradeCreditPointTotal MarksPercentageCGPAResultGenderSemesterBacklogTypeOverall Category
02201036LANDGE SAKSHI CHANGDEVARCHANASU00001852BASIC PROGRAMMING IN CBSC-DS101T122638A+292831.078.648.77PassFemale1NoTheoryDistinction
12201036LANDGE SAKSHI CHANGDEVARCHANASU00001852DATABASE MANAGEMENT SYSTEMBSC-DS102T122436A282831.078.648.77PassFemale1NoTheoryDistinction
22201036LANDGE SAKSHI CHANGDEVARCHANASU00001852ELEMENTS OF INFORMATION TECHNOLOGYBSC-DS103T121931A282831.078.648.77PassFemale1NoTheoryDistinction
32201036LANDGE SAKSHI CHANGDEVARCHANASU00001852INTRODUCTION TO R PROGRAMMINGBSC-DS104T102030A282831.078.648.77PassFemale1NoTheoryDistinction
42201036LANDGE SAKSHI CHANGDEVARCHANASU00001852INTRODUCTION TO DATABSC-DS105T112637A+292831.078.648.77PassFemale1NoTheoryDistinction
52201036LANDGE SAKSHI CHANGDEVARCHANASU00001852PROBABILITY AND BASIC STATISTICSBSC-DS106T112334A282831.078.648.77PassFemale1NoTheoryDistinction
62201036LANDGE SAKSHI CHANGDEVARCHANASU00001852MATRIX COMPUTATIONSBSC-DS107T122032A282831.078.648.77PassFemale1NoTheoryDistinction
72201036LANDGE SAKSHI CHANGDEVARCHANASU00001852DISCRETE MATHEMATICSBSC-DS108T133043A+292831.078.648.77PassFemale1NoTheoryDistinction
82201036LANDGE SAKSHI CHANGDEVARCHANASU00001852LAB COURSE ON 101BSC-DS109P122941A+292831.078.648.77PassFemale1NoPracticalDistinction
92201036LANDGE SAKSHI CHANGDEVARCHANASU00001852LAB COURSE ON 102BSC-DS110P122739A+292831.078.648.77PassFemale1NoPracticalDistinction
Seat NoNameMother's NamePRN NoSUBJECT NAMECODEInternalExternalTotalGradeCreditPointTotal MarksPercentageCGPAResultGenderSemesterBacklogTypeOverall Category
42782207074DAWARE PRAJAKTA SANJAYJOYTISU00001829DATA COMMUNICATION AND NETWORKINGBSC-DS311T102636A282693.074.818.51PassFemale3NoTheoryDistinction
42792207074DAWARE PRAJAKTA SANJAYJOYTISU00001829LAB COURSE ON 311BSC-DS312P102535A282693.074.818.51PassFemale3NoPracticalDistinction
42802207074DAWARE PRAJAKTA SANJAYJOYTISU00001829ARTIFICIAL INTELLIGENCE IN DATA SCIENCEBSC-DS 501T274875A+492693.074.818.51PassFemale5NoTheoryDistinction
42812207074DAWARE PRAJAKTA SANJAYJOYTISU00001829DATA VISUALIZATION AND DATA STORY\nTELLINGBSC-DS 502T284876A+492693.074.818.51PassFemale5NoTheoryDistinction
42822207074DAWARE PRAJAKTA SANJAYJOYTISU00001829CLOUD COMPUTING ESSENTIALSBSC-DS 503T305282A+492693.074.818.51PassFemale5NoTheoryDistinction
42832207074DAWARE PRAJAKTA SANJAYJOYTISU00001829LAB COURSE ON 501BSC-DS 505P122638A+292693.074.818.51PassFemale5NoPracticalDistinction
42842207074DAWARE PRAJAKTA SANJAYJOYTISU00001829LAB COURSE ON 502BSC-DS 506P143448O2102693.074.818.51PassFemale5NoPracticalDistinction
42852207074DAWARE PRAJAKTA SANJAYJOYTISU00001829LAB COURSE ON 503BSC-DS 507P143347O2102693.074.818.51PassFemale5NoPracticalDistinction
42862207074DAWARE PRAJAKTA SANJAYJOYTISU00001829ARTIFICIAL INTELLIGENCE ETHICSBSC-DS 509T103444A+292693.074.818.51PassFemale5NoTheoryDistinction
42872207074DAWARE PRAJAKTA SANJAYJOYTISU00001829LAB COURSE ON 509BSC-DS 510P143246O2102693.074.818.51PassFemale5NoPracticalDistinction